Autor: |
Li, Wei, Zhang, Liangchi, Chen, Xinping, Wu, Chuhan, Cui, Zhenxiang, Niu, Chao |
Předmět: |
|
Zdroj: |
International Journal of Advanced Manufacturing Technology; Jan2020, Vol. 112 Issue 3/4, p853-865, 13p, 3 Color Photographs, 1 Diagram, 6 Charts, 8 Graphs |
Abstrakt: |
This paper presents an artificial intelligence (AI) method for the evolution prediction of surface scratching in sheet metals subjected to contact sliding. Ball-on-disk sliding was employed, and ball diameter, normal load, surface roughness, sliding cycles and the maximum scratching depth in the metal sheet were taken as the fuzzy variables to assess the contributions of individual variables to the surface damage. To improve the prediction accuracy, the quantum-behaved particle swarm optimisation (QPSO) algorithm was further developed and utilised to refine the fuzzy model by optimising the membership functions of the fuzzy variables. It was found that this AI technique, which integrates the fuzzy set theory with the improved QPSO algorithm, can accurately, reliably and efficiently predict the surface scratching evolution, which is otherwise impossible to be implemented. [ABSTRACT FROM AUTHOR] |
Databáze: |
Complementary Index |
Externí odkaz: |
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|